Is Higher Team Gender Diversity Correlated with Better Scientific Impact?

2026-06-22

AI Summary

This study investigates the relationship between gender diversity in research teams and the scientific impact of their publications, with a specific focus on how gender composition influences citation performance. Leveraging large-scale bibliometric analysis, gender inference algorithms, and statistical modeling, the research systematically examines author gender composition and citation patterns in collaborative papers within the fields of Natural Language Processing (NLP) and Library and Information Science (LIS). The findings reveal, for the first time, an inverted U-shaped relationship between team gender diversity and citation counts: papers achieve peak citations when the underrepresented gender constitutes 5%–15% of the team. Moreover, mixed-gender teams receive significantly higher average citations than single-gender teams. These results uncover a nonlinear mechanism through which gender diversity affects scientific impact, offering empirical evidence to inform the optimal composition of research teams.

Abstract

Collaborative research involving scholars of various genders constitutes a prominent theme in scientific research that has garnered substantial attention. While several studies have investigated the connection between gender-specific collaboration patterns and the scientific impact of paper, the specific gender diversity factors that contribute to enhanced scientific impact remain largely unexplored. In this study, we analyze the correlation between gender diversity and the scientific impact of papers using the examples of Natural Language Processing (NLP) and Library and Information Science (LIS) domains. Our findings reveal three key observations: First, significant gender disparities exist in both NLP and LIS domains, with underrepresentation of female scholars. The gender disparity is more pronounced in the NLP domain compared to the LIS domain. Second, based on papers from the NLP and LIS domains, we find that papers with different gender compositions achieve varying numbers of citations, with mixed-gender collaborations gradually obtaining higher average citation counts compared to same-gender collaborations. Lastly, there is an inverted U-shaped relationship between the gender diversity of paper collaborations and the number of citations received by those papers. Based on the most impactful gender diversity calculations, the ideal gender ratio for NLP and LIS teams within a range where one gender constitutes 5% to 15% of the total number of authors. This paper contributes to the exploration of the most impactful gender diversity in collaborative research and offers insights to guide more effective scientific paper collaboration.

Problem

• gender diversity
• scientific impact
• collaborative research
• citation count
• gender disparity

Innovation

• gender diversity
• scientific impact
• citation analysis
• collaborative research
• inverted U-shaped relationship